DataAnalyzr constructor

The type of analysis to be performed.

analysis_type = "sql"

API key for accessing LLM services. May also be set as an environment variable.

api_key = "sk-apikey"

Dictionary of class parameters. Accepts max_retries, time_limit, and auto_train.

class_params = {
    "max_retries": 5, # int
    "time_limit": 60, # int
    "auto_train": True, # bool
}

LLM instance for generating analysis. Default LLM used is GPT-4o.

from lyzr.base import LyzrLLMFactory
generator_llm = LyzrLLMFactory.from_defaults(model="gpt-4-turbo")

For details on configuring the LLM, see the Large Language Models guide.

LLM instance for performing analysis. Default LLM used is GPT-4o.

from lyzr.base import LyzrLLMFactory
analysis_llm = LyzrLLMFactory.from_defaults(model="gpt-4o")

For details on configuring the LLM, see the Large Language Models guide.

Context for analysis and response generation. Default is an empty string.

• This context is used in the analysis, insights, recommendations, and tasks methods.
• When passed as a string, the same context is applied to all sections - analysis, insights, recommendations, and tasks.
• When passed as a dictionary, it should have keys analysis, visualisation, insights, recommendations, and tasks.
• To override the class context for a specific query, pass the context to the ask method.

context = "Sales data analysis for Q1 2023"
# or
context = {
    "analysis": "Sales data analysis for Q1 2023",
    "visualisation": "Distribution plot for sales data",
    "insights": "Key trends in sales data",
    "recommendations": "Improve sales performance",
    "tasks": "Analyze sales data",
}

Dictionary of logging parameters. Accepts log_filename, log_level, and print_log.

log_params = {
    "log_filename": "dataanalyzr.csv", # str
    "log_level": "INFO", # str
    "print_log": False, # bool
}

DataAnalyzr object for performing conversational data analysis.